Plant de novo genome sequencing data the germplasm (2021)

We investigated and collected the germplasm resources of cyanine in the Qinghai Tibet Plateau and its surrounding areas, carried out homogenous garden experiments to obtain phenotypic data, used genome sequencing technology to obtain data libraries and construct high-quality reference genomes. Using the re sequencing technology to analyze the structure of the cyanine population, combined with the early human migration and diffusion routes, this paper explores the historical process of the formation of the modern geographical distribution pattern of the cyanine on the Qinghai Tibet Plateau. By correlation analysis with phenotypic data, the adaptive mechanism of modern populations of cyanine was analyzed. Understand the environmental differences of the pan third pole and the impact of human activities and cultural differences in different regions on the migration, adaptation and domestication of plants on the Qinghai Tibet Plateau from the whole genome level.

0 2022-06-23

Plant Hi-C sequencing data of the germplasm (2021)

We investigated and collected the germplasm resources of cyanine in the Qinghai Tibet Plateau and its surrounding areas, carried out homogenous garden experiments to obtain phenotypic data, used genome sequencing technology to obtain data libraries and construct high-quality reference genomes. Using the re sequencing technology to analyze the structure of the cyanine population, combined with the early human migration and diffusion routes, this paper explores the historical process of the formation of the modern geographical distribution pattern of the cyanine on the Qinghai Tibet Plateau. By correlation analysis with phenotypic data, the adaptive mechanism of modern populations of cyanine was analyzed. Understand the environmental differences of the pan third pole and the impact of human activities and cultural differences in different regions on the migration, adaptation and domestication of plants on the Qinghai Tibet Plateau from the whole genome level.

0 2022-06-23

Plant genome resequencing data of the germplasm (2021)

We investigated and collected the germplasm resources of cyanine in the Qinghai Tibet Plateau and its surrounding areas, carried out homogenous garden experiments to obtain phenotypic data, used genome sequencing technology to obtain data libraries and construct high-quality reference genomes. Using the re sequencing technology to analyze the structure of the cyanine population, combined with the early human migration and diffusion routes, this paper explores the historical process of the formation of the modern geographical distribution pattern of the cyanine on the Qinghai Tibet Plateau. By correlation analysis with phenotypic data, the adaptive mechanism of modern populations of cyanine was analyzed. Understand the environmental differences of the pan third pole and the impact of human activities and cultural differences in different regions on the migration, adaptation and domestication of plants on the Qinghai Tibet Plateau from the whole genome level.

0 2022-06-23

Debris flow risk assessment in China Pakistan Economic Corridor (2021)

This data is the debris flow risk assessment data, which is obtained from the analysis and research of the debris flow disaster in the China Pakistan Economic Corridor. The sample data of debris flow is the detailed data of debris flow disaster through remote sensing interpretation and on-site verification. A risk assessment system is established to evaluate the debris flow risk in the study area by using the information method, and then the risk area is divided by using the natural breakpoint method. This data can be used to assess the risk of major debris flow disasters, understand the relationship between the risk degree of major debris flow, and provide scientific guidance for the decision-making of local government departments in disaster prevention and mitigation and urban governance.

0 2022-06-21

Distributions of debris flows in CPEC and Tianshan Mountain

This data provides the distribution of debris flows in the China-Pakistan Economic Corridor and the Tianshan Mountains by 2021. Based on historical data collection, field surveys and interpretation of remote sensing images, combined with digital topographic maps (DEM) and geological maps, the latest China-Pakistan economic The debris flow distribution data of the corridor (foreign section) has good reliability of data information, and the data can be used as the basic data for debris flow distribution law, debris flow risk, and risk calculation. The extraction of the debris flow basin mainly adopts the hydrological analysis method in ArcGIS, taking into account the accuracy limitation of DEM, combined with Google Earth images to perform necessary manual correction.

0 2022-06-20

Vulnerability assessment of debris flow in China Pakistan Economic Corridor (2021)

This data is the debris flow risk assessment data obtained from the analysis and Research on the debris flow disaster in the China Pakistan Economic Corridor, and the data source is the risk and vulnerability analysis results obtained from this study; The research method is based on the risk expression given by the United Nations Department of Humanitarian Affairs (1992): risk = hazard × Vulnerability, risk analysis of debris flow disaster in the study area.. The purpose of this data is to assess the risk of debris flow disaster in the China Pakistan Economic Corridor, understand the relationship between the intensity of major debris flow risk, and provide scientific guidance for the decision-making of local government departments in disaster prevention and mitigation and urban governance.

0 2022-06-20

Debris flow risk assessment in China Pakistan Economic Corridor (2021)

This data is the debris flow risk assessment data obtained from the analysis and Research on the debris flow disaster in the China Pakistan Economic Corridor, and the data source is the risk and vulnerability analysis results obtained from this study; The research method is based on the risk expression given by the United Nations Department of Humanitarian Affairs (1992): risk = hazard × Vulnerability, risk analysis of debris flow disaster in the study area.. The purpose of this data is to assess the risk of debris flow disaster in the China Pakistan Economic Corridor, understand the relationship between the intensity of major debris flow risk, and provide scientific guidance for the decision-making of local government departments in disaster prevention and mitigation and urban governance.

0 2022-06-20

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of Sidaoqiao Superstation-2021)

The dataset contains the phenological camera observation data of the Sidaoqiao Superstation in the downstream of Heihe integrated observatory network from May 2 to December 26, 2021. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc). Please refer to Liu et al. (2018) for sites information in the Citation section.

0 2022-06-16

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (phenology camera observation data set of A’rou Superstation-2021)

The dataset contains the phenological camera observation data of the Arou Superstation in the midstream of Heihe integrated observatory network from January 1 to December 31, 2021. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc). Please refer to Liu et al. (2018) for sites information in the Citation section.

0 2022-06-16

Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Leaf area index of Daman Superstation, 2021)

This dataset contains the LAI measurements from the Daman superstation in the middle reaches of the Heihe integrated observatory network from July 22 to September 5 in 2021. The site (100.376° E, 38.853°N) was located in the maize surface, near Zhangye city in Gansu Province. The elevation is 1556 m. There are 3 observation samples, each of which is about 30m×30m in size, and the latitude and longitude are (100.374°E, 38.855°N), (100.371° E, 38.854°N), (100.369°E, 38.854°N). Four sub-canopy nodes and one above-canopy node are arranged in each sample. The data is obtained from LAINet measurements; the four-steps are performed to obtain LAI: the raw data is light quantum (level 0); the daily LAI can be obtained using the software LAInet (level 1); further the invalid and null values are screened and using the 5 days moving averaged method to obtain the processed LAI (level 2); for the multi LAINet nodes observation, the averaged LAI of the nodes area is the final LAI (level 3). The released data are the post processed LAI products and stored using *.xls format. For more information, please refer to Liu et al. (2018) (for sites information), Qu et al. (2014) for data processing) in the Citation section.

0 2022-06-16